library(pacman)
pacman::p_load(edgeR, RColorBrewer, gplots, ggplot2, reshape2, DT, cowplot,
limma, DESeq, DESeq2, data.table, e1071, ComplexHeatmap)
source("https://raw.githubusercontent.com/CJinnny/RNA-seq-tutorial/master/RNA_seq_tutorial_functions.R")
This data came out of Kallisto software, gene counts are the sum of trancript pseudocounts. These counts are not in round numbers, so we need to round them to integers first.
Here I created my own transcriptome assembly using StringTie https://ccb.jhu.edu/software/stringtie/, hense the “MSTRG.” gene_id.
The reason I did that was because I not only want to study genes found in the reference genome, but also characterize new transcripts.
In order to know gene functions, we can convert MSTRG id into maize reference genome id (v4), which is already well characterized.
require(data.table)
data <- fread("https://raw.githubusercontent.com/CJinnny/RNA-seq-tutorial/master/genes.txt", sep = "\t")
data[,6:15] = round(data[,6:15])
id_conv <- fread("https://raw.githubusercontent.com/CJinnny/RNA-seq-tutorial/master/MSTRG_to_Zm_id.txt", header = FALSE, sep = "\t")
functions <- fread("https://raw.githubusercontent.com/CJinnny/RNA-seq-tutorial/master/B73v4_gene_function.txt", header = FALSE, sep = "\t")
functions <- subset(functions, select = c("V1", "V2"))
colnames(id_conv) <- c("MSTRG_gene_id", "v4_gene_id")
colnames(functions) <- c("v4_gene_id", "gene_function")
annotations = merge(id_conv, functions, by = "v4_gene_id", all.x = TRUE)
data <- merge(data, annotations, by = "MSTRG_gene_id", all.x = TRUE)
data <- aggregate(data,
by = list(data$MSTRG_gene_id),
FUN = aggregate_func
)
data <- na.omit(data, cols="v4_gene_id")
data <- data.frame(data, row.names = 1)
setnames(data, old = c("UU1", "UU2", "UU3", "UU4", "UU5", "WW1", "WW2", "WW3", "WW4", "WW5"),
new = c("U1", "U2","U3","U4","U5", "W1", "W2","W3","W4","W5"))
head(data)
## MSTRG_gene_id chr_name start end strand U1 U2 U3
## MSTRG.1000 MSTRG.1000 chr1 25047512 25050032 - 195 333 154
## MSTRG.10000 MSTRG.10000 chr10 32649346 32649418 + 0 0 0
## MSTRG.10002 MSTRG.10002 chr10 32585811 32588982 - 154 244 273
## MSTRG.10005 MSTRG.10005 chr10 32815479 32817141 + 0 0 0
## MSTRG.10007 MSTRG.10007 chr10 32529623 32567490 - 419 381 397
## MSTRG.1001 MSTRG.1001 chr1 25152267 25155144 + 139 152 150
## U4 U5 W1 W2 W3 W4 W5 v4_gene_id
## MSTRG.1000 166 105 117 52 126 123 81 Zm00001d028167
## MSTRG.10000 0 0 0 0 0 0 0 Zm00001d023970
## MSTRG.10002 106 242 226 212 274 218 212 Zm00001d023969
## MSTRG.10005 0 0 0 0 0 1 0 Zm00001d023973
## MSTRG.10007 223 395 423 345 406 435 375 Zm00001d023968
## MSTRG.1001 81 190 167 92 110 182 148 Zm00001d028171
## gene_function
## MSTRG.1000 Cytochrome b561 and DOMON domain-containing protein
## MSTRG.10000 NA
## MSTRG.10002 A/G-specific adenine DNA glycosylase
## MSTRG.10005 Cysteine proteinases superfamily protein
## MSTRG.10007 Glutathione S-transferase
## MSTRG.1001 Calcium-dependent lipid-binding (CaLB domain) family protein
counts <- data[,6:15]
require(e1071)
data_diagnosis(counts)
## skewness is:
## 40.72006 45.82615 23.87964 39.31152 25.54488 46.11351 37.21044 42.03261 29.757 61.20547
## kurtosis is:
## 2725.111 3708.717 1079.134 2462.335 1098.406 3271.049 2443.01 2906.647 1718.613 6269.872
Data is skewed: most values are around 0, but few outliers have high values.
We need to do data transformations for PCA plot, otherwise outliers will have a great impact on the clustering.
log transformation: logcounts = log2(counts + 1)
DESeq2::rlog(): “regularized log” transformation. For more information see https://rdrr.io/bioc/DESeq2/man/rlog.html
edgeR::cpm(): “counts per million” transformation. For more information see https://rdrr.io/bioc/edgeR/man/cpm.html
DESeq2:varianceStabilizingTransformation(): “variance stabilizing transformation”. For more information see https://rdrr.io/bioc/DESeq2/man/varianceStabilizingTransformation.html
require(DESeq2)
require(edgeR)
logcounts = log2(counts + 1)
rlogcounts = rlog(as.matrix(counts))
rownames(rlogcounts) = rownames(logcounts)
cpmcounts = cpm(as.matrix(counts), prior.count = 2, log = TRUE)
vstcounts = varianceStabilizingTransformation(as.matrix(counts))
data_diagnosis(logcounts)
## skewness is:
## -0.06744175 -0.06788012 -0.07223585 0.02643566 -0.06926398 -0.06282771 -0.002090134 -0.03638436 -0.05022853 -0.02295028
## kurtosis is:
## -1.45588 -1.470203 -1.478033 -1.442948 -1.466676 -1.457927 -1.481011 -1.476796 -1.485275 -1.481922
data_diagnosis(rlogcounts)
## skewness is:
## -0.107869 -0.1180331 -0.1177119 -0.1130238 -0.1137576 -0.1143162 -0.1122595 -0.1080767 -0.1176406 -0.1094335
## kurtosis is:
## -1.406649 -1.417596 -1.420769 -1.410637 -1.411501 -1.410185 -1.415751 -1.410148 -1.42091 -1.415035
data_diagnosis(cpmcounts)
## skewness is:
## 0.07228723 0.03930489 0.03457964 0.05780575 0.0485479 0.04905025 0.05757718 0.07313324 0.03478149 0.06931923
## kurtosis is:
## -1.418314 -1.44688 -1.460882 -1.429948 -1.437775 -1.430747 -1.461798 -1.442083 -1.467098 -1.453814
data_diagnosis(vstcounts)
## skewness is:
## 0.691676 0.6513708 0.6538715 0.6695807 0.6746568 0.6729289 0.6653166 0.6836064 0.6459285 0.6685212
## kurtosis is:
## -0.3981678 -0.5048652 -0.5732651 -0.4227583 -0.4496328 -0.4397634 -0.5113099 -0.4320609 -0.5695674 -0.5032185
require(graphics)
require(RColorBrewer)
par(mfrow=c(2,3), mar=c(5.1, 4.6, 4.1, 1.6))
draw_PCA(counts, title = "PCA on raw data")
draw_PCA(logcounts, title = "PCA on log transformed data")
draw_PCA(rlogcounts, title = "PCA on rlog transformed data")
draw_PCA(cpmcounts, title = "PCA on cpm transformed data")
draw_PCA(vstcounts, title = "PCA on vst transformed data")
par(mfrow=c(2,3))
plotMDS(counts, col = c(rep("red", 5), rep("blue", 5)), cex = 1.5)
title("MDS plot on raw data")
plotMDS(logcounts, col = c(rep("red", 5), rep("blue", 5)), cex = 1.5)
title("MDS plot on log transformed data")
plotMDS(rlogcounts, col = c(rep("red", 5), rep("blue", 5)), cex = 1.5)
title("MDS plot on rlog transformed data")
plotMDS(cpmcounts, col = c(rep("red", 5), rep("blue", 5)), cex = 1.5)
title("MDS plot on cpm transformed data")
plotMDS(vstcounts, col = c(rep("red", 5), rep("blue", 5)), cex = 1.5)
title("MDS plot on vst transformed data")
require(gplots)
draw_corr_heatmap(as.matrix(counts), show_cellnote = TRUE, title = "clustering sample-to-sample\n distance on raw data")
draw_corr_heatmap(as.matrix(logcounts), show_cellnote = TRUE, title = "clustering sample-to-sample\n distance on log transformed data")
draw_corr_heatmap(rlogcounts, show_cellnote = TRUE, title = "clustering sample-to-sample\n distance on rlog transformed data")
draw_corr_heatmap(cpmcounts, show_cellnote = TRUE, title = "clustering sample-to-sample\n distance on cpm transformed data")
draw_corr_heatmap(vstcounts, show_cellnote = TRUE, title = "clustering sample-to-sample\n distance on vst transformed data")
conditions = factor(c(rep("Ufo",5), rep("Wt",5)))
keep <- rowSums(cpm(counts)>1) >= 5
table(keep)
## keep
## FALSE TRUE
## 17671 22409
keep_true = data.frame(keep[which(keep == TRUE)])
filter = subset(counts, rownames(counts) %in% rownames(keep_true))
require(DESeq)
cds = newCountDataSet(filter, conditions)
cds = estimateSizeFactors(cds)
cds = estimateDispersions(cds)
DESeq_res = nbinomTest(cds, "Wt", "Ufo")
rownames(DESeq_res) = DESeq_res$id
DESeq_DE = subset(DESeq_res, (log2FoldChange < -1 & padj < 0.05) | (log2FoldChange > 1 & padj < 0.05) )
DESeq_nc = counts(cds, normalized = TRUE)
DESeq_nc = data.frame("id"=rownames(DESeq_nc), DESeq_nc)
DESeq_nc_DE = subset(DESeq_nc, id %in% DESeq_DE$id)
require(DESeq2)
colData = data.frame(samples=colnames(filter), conditions=conditions)
dds = DESeqDataSetFromMatrix(countData = filter, colData = colData, design = ~conditions)
## converting counts to integer mode
dds = DESeq(dds)
## estimating size factors
## estimating dispersions
## gene-wise dispersion estimates
## mean-dispersion relationship
## final dispersion estimates
## fitting model and testing
DESeq2_res = results(dds)
DESeq2_DE = subset(data.frame(DESeq2_res), (log2FoldChange < -1 & padj < 0.05) | (log2FoldChange > 1 & padj < 0.05) )
DESeq2_DE = data.frame("id"=rownames(DESeq2_DE), DESeq2_DE)
DESeq2_nc = counts(dds, normalized = TRUE)
DESeq2_nc = data.frame("id" = rownames(DESeq2_nc), DESeq2_nc)
DESeq2_nc_DE = subset(DESeq2_nc, id %in% DESeq2_DE$id)
require(edgeR)
group = as.vector(conditions)
dge = DGEList(counts = filter, group = group)
dge = calcNormFactors(dge)
dis = estimateCommonDisp(dge)
tag = estimateTagwiseDisp(dis)
etx = exactTest(tag)
edgeR_res = etx$table
edgeR_res$FDR = p.adjust(edgeR_res$PValue, method = "BH")
edgeR_DE = subset(edgeR_res, (logFC < -1 & FDR < 0.05) | (logFC > 1 & FDR < 0.05) )
edgeR_DE = data.frame("id" = rownames(edgeR_DE), edgeR_DE)
edgeR_nc = tag$pseudo.counts
edgeR_nc = data.frame("id"=rownames(edgeR_nc), edgeR_nc)
edgeR_nc_DE = subset(edgeR_nc, id %in% edgeR_DE$id)
require(limma)
design = model.matrix(~conditions)
voom = voom(filter, design, normalize="quantile")
fit = lmFit(voom, design)
fit = eBayes(fit)
limma_res = topTable(fit, coef = NULL, n=Inf)
## Removing intercept from test coefficients
limma_DE = subset(limma_res, (logFC < -1 & adj.P.Val < 0.05) | (logFC > 1 & adj.P.Val < 0.05) )
limma_DE = data.frame("id"=rownames(limma_DE), limma_DE)
limma_nc = 2**voom$E
limma_nc = data.frame("id"=rownames(limma_nc), limma_nc)
limma_nc_DE = subset(limma_nc, id %in% limma_DE$id)
dflist <- list(DESeq=DESeq_DE, DESeq2=DESeq2_DE, edgeR=edgeR_DE, limma=limma_DE)
Compare <- join_id(dflist)
## Loading required package: plyr
##
## Attaching package: 'plyr'
## The following object is masked from 'package:matrixStats':
##
## count
## The following object is masked from 'package:IRanges':
##
## desc
## The following object is masked from 'package:S4Vectors':
##
## rename
Compare$message
## [1] "There are 315 genes DE in all 4 methods 86 genes in 3, 186 genes in 2, 81 genes in 1."
library(DT)
summary <- merge(Compare$merged_table, annotations, by.x = "id", by.y = "MSTRG_gene_id", all.x = TRUE)
datatable(summary)
require(VennDiagram)
## Loading required package: VennDiagram
## Loading required package: futile.logger
draw_venndiagram(dflist, Compare$merged_table)
require(ComplexHeatmap)
Ht1 = DE_heatmap(DESeq_nc_DE, title = "DESeq", km = 2)
Ht2 = DE_heatmap(DESeq2_nc_DE, title = "DESeq2", km = 2)
Ht3 = DE_heatmap(edgeR_nc_DE, title = "edgeR", km = 2)
Ht4 = DE_heatmap(limma_nc_DE, title = "limma", km = 2)
Ht1
Ht2
Ht3
Ht4
Ht1 = DE_heatmap(DESeq_nc_DE, common_id=Compare$common_id, title = "DESeq", km = 2)
Ht2 = DE_heatmap(DESeq2_nc_DE, common_id=Compare$common_id, title = "DESeq2", km = 2)
Ht3 = DE_heatmap(edgeR_nc_DE, common_id=Compare$common_id, title = "edgeR", km = 2)
Ht4 = DE_heatmap(limma_nc_DE, common_id=Compare$common_id, title = "limma", km = 2)
Ht1 + Ht2 + Ht3 + Ht4
par(mfrow=c(2,2), mar=c(5.1, 4.6, 4.1, 1.6))
draw_MA(DESeq_res, type="DESeq")
draw_MA(DESeq2_res, type="DESeq2")
draw_MA(edgeR_res, type="edgeR")
draw_MA(limma_res, type="limma")
par(mfrow=c(2,2), mar=c(5.1, 4.6, 4.1, 1.6))
draw_volcano(DESeq_res, type="DESeq")
draw_volcano(DESeq2_res, type="DESeq2")
draw_volcano(edgeR_res, type="edgeR")
draw_volcano(limma_res, type="limma")
sessionInfo()
## R version 3.5.1 (2018-07-02)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS 10.14
##
## Matrix products: default
## BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
## LAPACK: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libLAPACK.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] grid stats4 parallel stats graphics grDevices utils
## [8] datasets methods base
##
## other attached packages:
## [1] VennDiagram_1.6.20 futile.logger_1.4.3
## [3] plyr_1.8.4 ComplexHeatmap_1.20.0
## [5] e1071_1.7-0 data.table_1.11.8
## [7] DESeq2_1.22.1 SummarizedExperiment_1.12.0
## [9] DelayedArray_0.8.0 BiocParallel_1.16.0
## [11] matrixStats_0.54.0 GenomicRanges_1.34.0
## [13] GenomeInfoDb_1.18.1 IRanges_2.16.0
## [15] S4Vectors_0.20.1 DESeq_1.34.0
## [17] lattice_0.20-38 locfit_1.5-9.1
## [19] Biobase_2.42.0 BiocGenerics_0.28.0
## [21] cowplot_0.9.3 DT_0.5
## [23] reshape2_1.4.3 ggplot2_3.1.0
## [25] gplots_3.0.1 RColorBrewer_1.1-2
## [27] pacman_0.5.0 edgeR_3.24.0
## [29] limma_3.38.2
##
## loaded via a namespace (and not attached):
## [1] colorspace_1.3-2 rjson_0.2.20 class_7.3-14
## [4] rprojroot_1.3-2 circlize_0.4.5 htmlTable_1.12
## [7] XVector_0.22.0 GlobalOptions_0.1.0 base64enc_0.1-3
## [10] rstudioapi_0.8 bit64_0.9-7 AnnotationDbi_1.44.0
## [13] splines_3.5.1 geneplotter_1.60.0 knitr_1.20
## [16] Formula_1.2-3 jsonlite_1.5 annotate_1.60.0
## [19] cluster_2.0.7-1 shiny_1.2.0 compiler_3.5.1
## [22] backports_1.1.2 assertthat_0.2.0 Matrix_1.2-15
## [25] lazyeval_0.2.1 formatR_1.5 later_0.7.5
## [28] acepack_1.4.1 htmltools_0.3.6 tools_3.5.1
## [31] bindrcpp_0.2.2 gtable_0.2.0 glue_1.3.0
## [34] GenomeInfoDbData_1.2.0 dplyr_0.7.8 Rcpp_1.0.0
## [37] gdata_2.18.0 crosstalk_1.0.0 stringr_1.3.1
## [40] mime_0.6 gtools_3.8.1 XML_3.98-1.16
## [43] zlibbioc_1.28.0 scales_1.0.0 promises_1.0.1
## [46] lambda.r_1.2.3 yaml_2.2.0 curl_3.2
## [49] memoise_1.1.0 gridExtra_2.3 rpart_4.1-13
## [52] latticeExtra_0.6-28 stringi_1.2.5 RSQLite_2.1.1
## [55] genefilter_1.64.0 checkmate_1.8.5 caTools_1.17.1.1
## [58] shape_1.4.4 rlang_0.3.0.1 pkgconfig_2.0.2
## [61] bitops_1.0-6 evaluate_0.12 purrr_0.2.5
## [64] bindr_0.1.1 htmlwidgets_1.3 labeling_0.3
## [67] bit_1.1-14 tidyselect_0.2.5 magrittr_1.5
## [70] R6_2.3.0 Hmisc_4.1-1 DBI_1.0.0
## [73] pillar_1.3.0 foreign_0.8-71 withr_2.1.2
## [76] survival_2.43-1 RCurl_1.95-4.11 nnet_7.3-12
## [79] tibble_1.4.2 crayon_1.3.4 futile.options_1.0.1
## [82] KernSmooth_2.23-15 rmarkdown_1.10 GetoptLong_0.1.7
## [85] blob_1.1.1 digest_0.6.18 xtable_1.8-3
## [88] httpuv_1.4.5 munsell_0.5.0